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Slot Fillers let you monitor every user input for specific Slot matches and store them in the Context object. This way, if an answer is already provided, the AI Agent skips the corresponding question in the Question Node. For example, if a user says I'm flying from San Francisco on United and the AI Agent asks What city are you departing from? — this question would be redundant and create a poor user experience. Instead, Slot Fillers ensure that only missing information is requested.

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Key Benefits

  • Collecting and Remembering Information. With Slot Fillers, all required data is gathered and retained throughout the conversation, reducing errors and making conversations with the AI Agent smoother and more natural.
  • Handling Different Input Types. Slot Fillers accept various formats, for example, dates and numbers, making it flexible for user inputs.
To learn more about the benefits of using Slot Fillers and how they work, watch this video:

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Restrictions

  • Slot Fillers fail when multiple values share the same Slot type, making it impossible to distinguish between them. For example, I'm flying from SFO to ORD on United. The AI Agent can’t distinguish between departure airport and arrival airport because both match airport_code. To avoid this restriction, you can:
    • Create separate Slot Fillers for each value:
      • Departure Airport — use a Context Key, for example, departure_city.
      • Arrival Airport — use a Context Key, for example, arrival_city.
    • Modify the Flow to ask distinct questions for each Slot instead of extracting both from a single user input.

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Working with Slot Fillers

  • GUI
  • API
You can create, edit, and delete Slot Fillers in NLU > Slot Fillers in the Flow editor.

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Slot Filler Settings

FieldDescriptionExample
NameThe display name of the Slot Filler, used for identification.Departure Airport Slot
TypeDefines the type of input expected. You can select from the following Slot types:
- Slot – a Lexicon Slot. Make sure a Lexicon is attached and that this Slot is included in it.
- Regular Expression – a Regex Slot.
- Intent – extracts values based on recognized Intents. Useful when Slot filling is tied to Intent classification.
- System Slot – predefined Slot types that automatically extract common entity types such as dates, numbers, temperatures, and more.		-
Slot NameThis parameter appears if the Slot type is selected. Specifies which Slot this Filler is associated with.airport_code, date, name
Regular ExpressionThis parameter appears if the Regular Expression type is selected. Enter the regular expression to extract specific patterns from user input. Regex expressions must start with / and end with /g for global matching, for example, /^\d{3}-\d{3}-\d{4}$/g.
Context KeyThe variable name used to store the extracted value in the Context object.departure_city, arrival_date
FieldDescriptionExample
Additional ValidationAllows extra validation rules to ensure correct formatting or values.Ensure date format is YYYY-MM-DD.
Result LocationDetermines where the extracted data is stored.Context or Contact Profile.
Store result in ContextStores the extracted value in the Context object."departure_city": "SFO".
Store result in Contact ProfileStores the extracted value in the Contact Profile for future interactions.Store “preferred_airline” for returning users.
Store Detailed ResultsStores metadata about the extracted Slot, for example, confidence scores.Confidence score of 0.92 for "ORD".
Use Positive Keyphrases OnlyLimits Slot extraction to predefined keyphrases.Only accept "San Francisco" for departure_city.
Skip if the result is already in ContextPrevents asking for the Slot again if a valid value already exists in the Context object.If "departure_city": "LAX" is set, don’t ask again.

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Example

  1. Create a new Flow called Slot Fillers.
  2. Navigate to the NLU tab and attach relevant Lexicons, for example, airports and airlines.
  3. On the Slot Fillers tab, follow these steps:
    1. Create a new Slot Filler named Airline.
    2. Select Slot from the Type list.
  4. Enter airline in both the Slot name and Context Key fields.
  5. Repeat the steps 3.1 and 3.2 to create another Slot Filler named Airport, enter airport_code in the Slot name and Context Key fields.
  6. Click Build Model.
  1. Create a Question Node, enter What is your departure airport? in the Text field and select Slot from the Question Type list.
  2. Enter airport_code in Slot Name and Context Key to use.
  3. Enable Skip if answer exists in Context.
  4. Repeat the process for What is your airline?, enter airline in the Slot name and Context Key fields.
Provide all information at once:
User Input: I'm leaving San Francisco on United.
Result: No additional questions are asked.

Provide partial information:
User Input: I'm flying on United.
AI Agent: What is your departure airport?
User Input: SFO
Result: The AI Agent skips asking for the airline.

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More Information